Overview

Dataset statistics

Number of variables22
Number of observations11145
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory176.0 B

Variable types

NUM14
CAT8

Warnings

player_name has a high cardinality: 2235 distinct values High cardinality
college has a high cardinality: 316 distinct values High cardinality
country has a high cardinality: 76 distinct values High cardinality
draft_number has a high cardinality: 75 distinct values High cardinality
draft_number is highly correlated with draft_roundHigh correlation
draft_round is highly correlated with draft_numberHigh correlation
Unnamed: 0 has unique values Unique
pts has 136 (1.2%) zeros Zeros
reb has 118 (1.1%) zeros Zeros
ast has 374 (3.4%) zeros Zeros
oreb_pct has 425 (3.8%) zeros Zeros
dreb_pct has 162 (1.5%) zeros Zeros
ts_pct has 136 (1.2%) zeros Zeros
ast_pct has 359 (3.2%) zeros Zeros

Reproduction

Analysis started2020-12-12 13:28:55.461336
Analysis finished2020-12-12 13:29:31.599871
Duration36.14 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIQUE

Distinct11145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5572
Minimum0
Maximum11144
Zeros1
Zeros (%)< 0.1%
Memory size87.1 KiB
2020-12-12T15:29:31.678890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile557.2
Q12786
median5572
Q38358
95-th percentile10586.8
Maximum11144
Range11144
Interquartile range (IQR)5572

Descriptive statistics

Standard deviation3217.428709
Coefficient of variation (CV)0.5774279809
Kurtosis-1.2
Mean5572
Median Absolute Deviation (MAD)2786
Skewness0
Sum62099940
Variance10351847.5
MonotocityStrictly increasing
2020-12-12T15:29:31.818921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
12981< 0.1%
 
54081< 0.1%
 
95021< 0.1%
 
33551< 0.1%
 
13061< 0.1%
 
74491< 0.1%
 
54001< 0.1%
 
94941< 0.1%
 
33471< 0.1%
 
Other values (11135)1113599.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
111441< 0.1%
 
111431< 0.1%
 
111421< 0.1%
 
111411< 0.1%
 
111401< 0.1%
 

player_name
Categorical

HIGH CARDINALITY

Distinct2235
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
Vince Carter
 
22
Dirk Nowitzki
 
21
Kevin Garnett
 
20
Kobe Bryant
 
20
Jason Terry
 
19
Other values (2230)
11043 
ValueCountFrequency (%) 
Vince Carter220.2%
 
Dirk Nowitzki210.2%
 
Kevin Garnett200.2%
 
Kobe Bryant200.2%
 
Jason Terry190.2%
 
Tim Duncan190.2%
 
Paul Pierce190.2%
 
Jamal Crawford190.2%
 
Tyson Chandler190.2%
 
Steve Nash180.2%
 
Other values (2225)1094998.2%
 
2020-12-12T15:29:31.989959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique529 ?
Unique (%)4.7%
2020-12-12T15:29:32.154996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length13
Mean length12.80062808
Min length4
Distinct36
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
CLE
 
390
TOR
 
390
LAC
 
389
MIA
 
387
DAL
 
384
Other values (31)
9205 
ValueCountFrequency (%) 
CLE3903.5%
 
TOR3903.5%
 
LAC3893.5%
 
MIA3873.5%
 
DAL3843.4%
 
ATL3833.4%
 
PHI3803.4%
 
WAS3793.4%
 
HOU3783.4%
 
SAS3773.4%
 
Other values (26)730865.6%
 
2020-12-12T15:29:32.336046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:29:32.480079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

age
Real number (ℝ≥0)

Distinct27
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.16868551
Minimum18
Maximum44
Zeros0
Zeros (%)0.0%
Memory size87.1 KiB
2020-12-12T15:29:32.596105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q124
median27
Q330
95-th percentile35
Maximum44
Range26
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.344163672
Coefficient of variation (CV)0.1598959828
Kurtosis-0.2615013415
Mean27.16868551
Median Absolute Deviation (MAD)3
Skewness0.5458567812
Sum302795
Variance18.87175801
MonotocityNot monotonic
2020-12-12T15:29:32.730135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%) 
24114310.3%
 
2310399.3%
 
2510259.2%
 
269338.4%
 
278918.0%
 
287746.9%
 
297186.4%
 
226786.1%
 
306545.9%
 
315815.2%
 
Other values (17)270924.3%
 
ValueCountFrequency (%) 
183< 0.1%
 
19670.6%
 
202462.2%
 
214253.8%
 
226786.1%
 
ValueCountFrequency (%) 
441< 0.1%
 
433< 0.1%
 
423< 0.1%
 
4170.1%
 
40270.2%
 

player_height
Real number (ℝ≥0)

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.8128183
Minimum160.02
Maximum231.14
Zeros0
Zeros (%)0.0%
Memory size87.1 KiB
2020-12-12T15:29:32.866166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum160.02
5-th percentile185.42
Q1195.58
median200.66
Q3208.28
95-th percentile213.36
Maximum231.14
Range71.12
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation9.190973456
Coefficient of variation (CV)0.04576885845
Kurtosis-0.1395888697
Mean200.8128183
Median Absolute Deviation (MAD)7.62
Skewness-0.3775857815
Sum2238058.86
Variance84.47399307
MonotocityNot monotonic
2020-12-12T15:29:32.996195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%) 
205.74136912.3%
 
208.28113310.2%
 
200.6610939.8%
 
203.210619.5%
 
210.829588.6%
 
198.129188.2%
 
195.588077.2%
 
190.57556.8%
 
213.366736.0%
 
193.046195.6%
 
Other values (20)175915.8%
 
ValueCountFrequency (%) 
160.025< 0.1%
 
165.1130.1%
 
167.641< 0.1%
 
175.26240.2%
 
177.8520.5%
 
ValueCountFrequency (%) 
231.143< 0.1%
 
228.6160.1%
 
226.0660.1%
 
223.5270.1%
 
220.98430.4%
 

player_weight
Real number (ℝ≥0)

Distinct157
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.6378677
Minimum60.327736
Maximum163.29312
Zeros0
Zeros (%)0.0%
Memory size87.1 KiB
2020-12-12T15:29:33.155231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum60.327736
5-th percentile81.64656
Q190.7184
median99.79024
Q3109.315672
95-th percentile120.20188
Maximum163.29312
Range102.965384
Interquartile range (IQR)18.597272

Descriptive statistics

Standard deviation12.57629495
Coefficient of variation (CV)0.1249658328
Kurtosis-0.1286903686
Mean100.6378677
Median Absolute Deviation (MAD)9.07184
Skewness0.1469057176
Sum1121609.036
Variance158.1631947
MonotocityNot monotonic
2020-12-12T15:29:33.573354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
99.790245955.3%
 
108.862085545.0%
 
95.254325244.7%
 
102.05825124.6%
 
97.522285054.5%
 
106.594125004.5%
 
111.130044794.3%
 
113.3984724.2%
 
104.326164634.2%
 
90.71844474.0%
 
Other values (147)609454.7%
 
ValueCountFrequency (%) 
60.327736130.1%
 
61.6885121< 0.1%
 
63.9564724< 0.1%
 
65.770841< 0.1%
 
68.03882< 0.1%
 
ValueCountFrequency (%) 
163.293121< 0.1%
 
155.5820561< 0.1%
 
154.221281< 0.1%
 
151.953321< 0.1%
 
149.685361< 0.1%
 

college
Categorical

HIGH CARDINALITY

Distinct316
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
None
1684 
Kentucky
 
360
Duke
 
331
North Carolina
 
318
UCLA
 
280
Other values (311)
8172 
ValueCountFrequency (%) 
None168415.1%
 
Kentucky3603.2%
 
Duke3313.0%
 
North Carolina3182.9%
 
UCLA2802.5%
 
Arizona2572.3%
 
Kansas2512.3%
 
Connecticut2202.0%
 
Georgia Tech1801.6%
 
Florida1731.6%
 
Other values (306)709163.6%
 
2020-12-12T15:29:33.757830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique47 ?
Unique (%)0.4%
2020-12-12T15:29:33.934869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length39
Median length8
Mean length8.976312248
Min length1

country
Categorical

HIGH CARDINALITY

Distinct76
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
USA
9410 
France
 
153
Canada
 
140
Spain
 
79
Brazil
 
78
Other values (71)
1285 
ValueCountFrequency (%) 
USA941084.4%
 
France1531.4%
 
Canada1401.3%
 
Spain790.7%
 
Brazil780.7%
 
Australia740.7%
 
Slovenia670.6%
 
Turkey630.6%
 
Croatia620.6%
 
Argentina600.5%
 
Other values (66)9598.6%
 
2020-12-12T15:29:34.111912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6 ?
Unique (%)0.1%
2020-12-12T15:29:34.264947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length3
Mean length3.842530283
Min length3

draft_year
Categorical

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
Undrafted
1942 
1998
 
454
2003
 
430
2005
 
420
1996
 
406
Other values (40)
7493 
ValueCountFrequency (%) 
Undrafted194217.4%
 
19984544.1%
 
20034303.9%
 
20054203.8%
 
19964063.6%
 
20014033.6%
 
20083813.4%
 
19993663.3%
 
20003643.3%
 
20043623.2%
 
Other values (35)561750.4%
 
2020-12-12T15:29:34.433985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2020-12-12T15:29:34.594021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length4
Mean length4.87124271
Min length4

draft_round
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
1
6513 
2
2629 
Undrafted
1959 
3
 
20
4
 
12
Other values (3)
 
12
ValueCountFrequency (%) 
1651358.4%
 
2262923.6%
 
Undrafted195917.6%
 
3200.2%
 
4120.1%
 
75< 0.1%
 
65< 0.1%
 
82< 0.1%
 
2020-12-12T15:29:34.724049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:29:34.820071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:35.021116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length1
Mean length2.406191117
Min length1

draft_number
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct75
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
Undrafted
1959 
1
 
320
5
 
320
4
 
311
3
 
299
Other values (70)
7936 
ValueCountFrequency (%) 
Undrafted195917.6%
 
13202.9%
 
53202.9%
 
43112.8%
 
32992.7%
 
22992.7%
 
92822.5%
 
102782.5%
 
72722.4%
 
82582.3%
 
Other values (65)654758.7%
 
2020-12-12T15:29:35.195766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)0.1%
2020-12-12T15:29:35.366804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length2
Mean length3.001256169
Min length1

gp
Real number (ℝ≥0)

Distinct85
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.00583221
Minimum1
Maximum85
Zeros0
Zeros (%)0.0%
Memory size87.1 KiB
2020-12-12T15:29:35.510837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q132
median58
Q374
95-th percentile82
Maximum85
Range84
Interquartile range (IQR)42

Descriptive statistics

Standard deviation25.06949533
Coefficient of variation (CV)0.4820516136
Kurtosis-0.964908455
Mean52.00583221
Median Absolute Deviation (MAD)19
Skewness-0.5580053215
Sum579605
Variance628.4795963
MonotocityNot monotonic
2020-12-12T15:29:35.654870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
827196.5%
 
814103.7%
 
803913.5%
 
792732.4%
 
782642.4%
 
762372.1%
 
772332.1%
 
752302.1%
 
742262.0%
 
722151.9%
 
Other values (75)794771.3%
 
ValueCountFrequency (%) 
11020.9%
 
21361.2%
 
31301.2%
 
4980.9%
 
51191.1%
 
ValueCountFrequency (%) 
853< 0.1%
 
841< 0.1%
 
83100.1%
 
827196.5%
 
814103.7%
 

pts
Real number (ℝ≥0)

ZEROS

Distinct308
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.126487214
Minimum0
Maximum36.1
Zeros136
Zeros (%)1.2%
Memory size87.1 KiB
2020-12-12T15:29:35.811922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2
Q13.5
median6.6
Q311.5
95-th percentile20.1
Maximum36.1
Range36.1
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.935482275
Coefficient of variation (CV)0.730387204
Kurtosis0.792428321
Mean8.126487214
Median Absolute Deviation (MAD)3.6
Skewness1.057005413
Sum90569.7
Variance35.22994984
MonotocityNot monotonic
2020-12-12T15:29:35.962423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
21451.3%
 
01361.2%
 
41361.2%
 
31221.1%
 
51191.1%
 
3.31171.0%
 
4.21081.0%
 
4.51071.0%
 
11061.0%
 
2.51050.9%
 
Other values (298)994489.2%
 
ValueCountFrequency (%) 
01361.2%
 
0.13< 0.1%
 
0.270.1%
 
0.3210.2%
 
0.4180.2%
 
ValueCountFrequency (%) 
36.11< 0.1%
 
35.41< 0.1%
 
34.51< 0.1%
 
331< 0.1%
 
32.11< 0.1%
 

reb
Real number (ℝ≥0)

ZEROS

Distinct154
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.560035891
Minimum0
Maximum16.3
Zeros118
Zeros (%)1.1%
Memory size87.1 KiB
2020-12-12T15:29:36.129461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7
Q11.8
median3
Q34.7
95-th percentile8.7
Maximum16.3
Range16.3
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation2.495393818
Coefficient of variation (CV)0.700946253
Kurtosis1.861150883
Mean3.560035891
Median Absolute Deviation (MAD)1.4
Skewness1.291627447
Sum39676.6
Variance6.226990307
MonotocityNot monotonic
2020-12-12T15:29:36.272496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
22902.6%
 
1.82732.4%
 
1.92622.4%
 
2.32532.3%
 
1.52412.2%
 
12402.2%
 
2.42362.1%
 
2.12322.1%
 
1.72282.0%
 
1.62252.0%
 
Other values (144)866577.7%
 
ValueCountFrequency (%) 
01181.1%
 
0.1100.1%
 
0.2410.4%
 
0.3950.9%
 
0.4630.6%
 
ValueCountFrequency (%) 
16.31< 0.1%
 
16.11< 0.1%
 
161< 0.1%
 
15.61< 0.1%
 
15.41< 0.1%
 

ast
Real number (ℝ≥0)

ZEROS

Distinct114
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.801462539
Minimum0
Maximum11.7
Zeros374
Zeros (%)3.4%
Memory size87.1 KiB
2020-12-12T15:29:36.428530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.6
median1.2
Q32.4
95-th percentile5.6
Maximum11.7
Range11.7
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.789940237
Coefficient of variation (CV)0.9936039179
Kurtosis4.172886026
Mean1.801462539
Median Absolute Deviation (MAD)0.8
Skewness1.883540544
Sum20077.3
Variance3.203886052
MonotocityNot monotonic
2020-12-12T15:29:36.584565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.35925.3%
 
0.45494.9%
 
0.55234.7%
 
0.64774.3%
 
0.84524.1%
 
0.74524.1%
 
0.94414.0%
 
14363.9%
 
0.24013.6%
 
1.13863.5%
 
Other values (104)643657.7%
 
ValueCountFrequency (%) 
03743.4%
 
0.12792.5%
 
0.24013.6%
 
0.35925.3%
 
0.45494.9%
 
ValueCountFrequency (%) 
11.72< 0.1%
 
11.62< 0.1%
 
11.51< 0.1%
 
11.42< 0.1%
 
11.22< 0.1%
 

net_rating
Real number (ℝ)

Distinct707
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.153898609
Minimum-200
Maximum300
Zeros64
Zeros (%)0.6%
Memory size87.1 KiB
2020-12-12T15:29:36.741604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-16.98
Q1-6.3
median-1.3
Q33.2
95-th percentile10.2
Maximum300
Range500
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation12.15061141
Coefficient of variation (CV)-5.641217909
Kurtosis82.249367
Mean-2.153898609
Median Absolute Deviation (MAD)4.8
Skewness0.2830850421
Sum-24005.2
Variance147.6373576
MonotocityNot monotonic
2020-12-12T15:29:36.894638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.2760.7%
 
-0.3760.7%
 
-3.7740.7%
 
0.1730.7%
 
-1.4720.6%
 
-1.9720.6%
 
0.8710.6%
 
2.6710.6%
 
1.3710.6%
 
-0.1710.6%
 
Other values (697)1041893.5%
 
ValueCountFrequency (%) 
-2001< 0.1%
 
-158.31< 0.1%
 
-1501< 0.1%
 
-147.51< 0.1%
 
-144.91< 0.1%
 
ValueCountFrequency (%) 
3001< 0.1%
 
2501< 0.1%
 
1501< 0.1%
 
1201< 0.1%
 
114.31< 0.1%
 

oreb_pct
Real number (ℝ≥0)

ZEROS

Distinct216
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05559309107
Minimum0
Maximum1
Zeros425
Zeros (%)3.8%
Memory size87.1 KiB
2020-12-12T15:29:37.049204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.007
Q10.022
median0.043
Q30.086
95-th percentile0.131
Maximum1
Range1
Interquartile range (IQR)0.064

Descriptive statistics

Standard deviation0.04388855935
Coefficient of variation (CV)0.7894606776
Kurtosis26.01645954
Mean0.05559309107
Median Absolute Deviation (MAD)0.027
Skewness2.338951486
Sum619.585
Variance0.001926205641
MonotocityNot monotonic
2020-12-12T15:29:37.205239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04253.8%
 
0.0172111.9%
 
0.0232011.8%
 
0.0211971.8%
 
0.0151961.8%
 
0.0191941.7%
 
0.0141911.7%
 
0.0181881.7%
 
0.0161871.7%
 
0.0221741.6%
 
Other values (206)898180.6%
 
ValueCountFrequency (%) 
04253.8%
 
0.0022< 0.1%
 
0.00390.1%
 
0.004110.1%
 
0.005270.2%
 
ValueCountFrequency (%) 
11< 0.1%
 
0.61< 0.1%
 
0.53< 0.1%
 
0.42< 0.1%
 
0.3751< 0.1%
 

dreb_pct
Real number (ℝ≥0)

ZEROS

Distinct351
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1417719157
Minimum0
Maximum1
Zeros162
Zeros (%)1.5%
Memory size87.1 KiB
2020-12-12T15:29:37.363780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.063
Q10.096
median0.132
Q30.182
95-th percentile0.25
Maximum1
Range1
Interquartile range (IQR)0.086

Descriptive statistics

Standard deviation0.06319421645
Coefficient of variation (CV)0.4457456623
Kurtosis8.199562783
Mean0.1417719157
Median Absolute Deviation (MAD)0.041
Skewness1.254490105
Sum1580.048
Variance0.003993508992
MonotocityNot monotonic
2020-12-12T15:29:37.522816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01621.5%
 
0.11061.0%
 
0.0981030.9%
 
0.0861030.9%
 
0.1111020.9%
 
0.0911000.9%
 
0.1011000.9%
 
0.102990.9%
 
0.089980.9%
 
0.095970.9%
 
Other values (341)1007590.4%
 
ValueCountFrequency (%) 
01621.5%
 
0.0142< 0.1%
 
0.0151< 0.1%
 
0.0161< 0.1%
 
0.0171< 0.1%
 
ValueCountFrequency (%) 
12< 0.1%
 
0.7141< 0.1%
 
0.61< 0.1%
 
0.5711< 0.1%
 
0.5110.1%
 

usg_pct
Real number (ℝ≥0)

Distinct341
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.185599013
Minimum0
Maximum1
Zeros33
Zeros (%)0.3%
Memory size87.1 KiB
2020-12-12T15:29:37.684853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.108
Q10.15
median0.182
Q30.218
95-th percentile0.276
Maximum1
Range1
Interquartile range (IQR)0.068

Descriptive statistics

Standard deviation0.05304665234
Coefficient of variation (CV)0.2858132243
Kurtosis7.401879071
Mean0.185599013
Median Absolute Deviation (MAD)0.034
Skewness0.8327037378
Sum2068.501
Variance0.002813947325
MonotocityNot monotonic
2020-12-12T15:29:37.842888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.1831111.0%
 
0.1721050.9%
 
0.1861020.9%
 
0.191020.9%
 
0.1681020.9%
 
0.1821010.9%
 
0.1741000.9%
 
0.184990.9%
 
0.179990.9%
 
0.199980.9%
 
Other values (331)1012690.9%
 
ValueCountFrequency (%) 
0330.3%
 
0.0191< 0.1%
 
0.021< 0.1%
 
0.0251< 0.1%
 
0.031< 0.1%
 
ValueCountFrequency (%) 
11< 0.1%
 
0.751< 0.1%
 
0.5951< 0.1%
 
0.5711< 0.1%
 
0.53< 0.1%
 

ts_pct
Real number (ℝ≥0)

ZEROS

Distinct529
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5080993271
Minimum0
Maximum1.5
Zeros136
Zeros (%)1.2%
Memory size87.1 KiB
2020-12-12T15:29:38.001447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.357
Q10.478
median0.521
Q30.557
95-th percentile0.615
Maximum1.5
Range1.5
Interquartile range (IQR)0.079

Descriptive statistics

Standard deviation0.09887899176
Coefficient of variation (CV)0.1946056342
Kurtosis12.2434486
Mean0.5080993271
Median Absolute Deviation (MAD)0.039
Skewness-1.474998247
Sum5662.767
Variance0.009777055011
MonotocityNot monotonic
2020-12-12T15:29:38.145480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01361.2%
 
0.51131.0%
 
0.53980.9%
 
0.526970.9%
 
0.521940.8%
 
0.533940.8%
 
0.538930.8%
 
0.534870.8%
 
0.51870.8%
 
0.549870.8%
 
Other values (519)1015991.2%
 
ValueCountFrequency (%) 
01361.2%
 
0.0461< 0.1%
 
0.0511< 0.1%
 
0.0562< 0.1%
 
0.0911< 0.1%
 
ValueCountFrequency (%) 
1.53< 0.1%
 
1.06470.1%
 
1.051< 0.1%
 
1.0421< 0.1%
 
1.0251< 0.1%
 

ast_pct
Real number (ℝ≥0)

ZEROS

Distinct479
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1310775236
Minimum0
Maximum1
Zeros359
Zeros (%)3.2%
Memory size87.1 KiB
2020-12-12T15:29:38.309521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.024
Q10.065
median0.102
Q30.178
95-th percentile0.32
Maximum1
Range1
Interquartile range (IQR)0.113

Descriptive statistics

Standard deviation0.09501717881
Coefficient of variation (CV)0.7248929964
Kurtosis3.217573399
Mean0.1310775236
Median Absolute Deviation (MAD)0.047
Skewness1.388177292
Sum1460.859
Variance0.00902826427
MonotocityNot monotonic
2020-12-12T15:29:38.470558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03593.2%
 
0.0681030.9%
 
0.0631030.9%
 
0.077960.9%
 
0.1930.8%
 
0.078910.8%
 
0.051870.8%
 
0.083860.8%
 
0.074860.8%
 
0.087850.8%
 
Other values (469)995689.3%
 
ValueCountFrequency (%) 
03593.2%
 
0.0041< 0.1%
 
0.0052< 0.1%
 
0.0074< 0.1%
 
0.0085< 0.1%
 
ValueCountFrequency (%) 
13< 0.1%
 
0.751< 0.1%
 
0.6674< 0.1%
 
0.6361< 0.1%
 
0.6151< 0.1%
 

season
Categorical

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
2017-18
 
540
2018-19
 
530
2019-20
 
514
2014-15
 
492
2016-17
 
486
Other values (19)
8583 
ValueCountFrequency (%) 
2017-185404.8%
 
2018-195304.8%
 
2019-205144.6%
 
2014-154924.4%
 
2016-174864.4%
 
2013-144824.3%
 
2011-124784.3%
 
2015-164764.3%
 
2012-134694.2%
 
2004-054644.2%
 
Other values (14)621455.8%
 
2020-12-12T15:29:38.639596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:29:38.774626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7

Interactions

2020-12-12T15:29:00.903328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:01.067365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:01.210411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:01.366446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:01.510478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:01.648522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:01.800557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:01.956595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:02.101627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:02.243167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:02.386200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:02.527231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:02.672783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:02.936842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:03.079875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:03.221906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:03.362938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:03.514973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:03.657005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:03.792035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:03.941069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:04.095104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:04.238135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:04.380167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:04.522202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:04.664234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:04.805269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:04.956305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:05.099337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:05.255385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:05.409420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:05.576460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:05.733123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:05.882157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:06.047196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:06.216239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:06.374274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:06.530310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:06.687344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:06.843380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:06.999419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:07.165455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:07.320491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:07.463522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:07.606555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:07.759006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:07.902038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:08.037663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:08.186697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:08.339733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:08.483765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:08.626797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:08.768866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:08.910920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:09.052556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:09.203591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:09.344721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:09.477382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:09.610553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:09.754634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:09.886688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:10.012739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:10.153800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:10.298931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:10.434961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:10.567992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:10.701896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:10.834926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:10.967960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:11.255481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:11.389025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:11.541504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:11.692078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:11.854115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:12.008150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:12.153182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:12.313138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:12.479176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:12.633211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:12.785245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:12.937279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:13.088327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:13.239372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:13.399397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:13.550434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:13.708499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:13.863533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:14.031571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:14.189607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:14.339640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:14.504692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:14.673730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:14.832780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:14.989816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:15.146853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:15.302888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:15.459924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:15.627961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:15.784998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:15.939032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:16.095572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:16.254625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:16.402658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:16.542692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:16.698730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:16.858766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:17.009804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:17.159842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:17.308386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:17.458415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:17.607451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:17.764992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:17.911404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:18.056436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:18.197975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:18.354010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:18.499042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:18.637077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:18.789351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:18.946266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:19.092299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:19.236331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:19.380367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:19.524399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:19.671014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:19.824052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:19.968085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:20.112132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:20.255163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:20.412199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:20.556231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:20.694264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:20.846299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:21.003334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:21.151368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:21.295400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:21.438442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:21.778313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:21.924345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:22.079381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:22.225413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:22.375447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:22.518479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:22.672514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:22.817547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:22.956084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:23.107150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:23.261677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:23.406782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:23.548809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:23.693835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:23.835945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:23.979555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:24.132592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:24.274624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:24.417656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:24.558691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:24.712231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:24.855263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:24.991294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:25.142327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:25.296391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:25.440424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:25.584455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:25.728489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:25.872521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:26.014553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:26.168091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:26.312123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:26.468158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:26.624193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:26.791231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:26.947774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:27.095807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:27.259844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:27.426882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:27.586918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:27.742953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:27.899988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:28.057024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:28.214060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:28.378096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:28.533143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:28.677182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:28.820215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:28.976250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:29.120282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:29.256313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:29.406347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:29.563382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:29.709415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:29.854457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:29.998489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:30.142534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:30.286566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:30.438601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-12T15:29:38.894653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T15:29:39.179722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T15:29:39.468787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T15:29:39.765854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T15:29:40.085926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T15:29:30.740669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T15:29:31.372820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

Unnamed: 0player_nameteam_abbreviationageplayer_heightplayer_weightcollegecountrydraft_yeardraft_rounddraft_numbergpptsrebastnet_ratingoreb_pctdreb_pctusg_pctts_pctast_pctseason
00Dennis RodmanCHI36.0198.1299.790240Southeastern Oklahoma StateUSA1986227555.716.13.116.10.1860.3230.1000.4790.1131996-97
11Dwayne SchintziusLAC28.0215.90117.933920FloridaUSA1990124152.31.50.312.30.0780.1510.1750.4300.0481996-97
22Earl CuretonTOR39.0205.7495.254320Detroit MercyUSA197935890.81.00.4-2.10.1050.1020.1030.3760.1481996-97
33Ed O'BannonDAL24.0203.20100.697424UCLAUSA199519643.72.30.6-8.70.0600.1490.1670.3990.0771996-97
44Ed PinckneyMIA34.0205.74108.862080VillanovaUSA1985110272.42.40.2-11.20.1090.1790.1270.6110.0401996-97
55Eddie JohnsonHOU38.0200.6697.522280IllinoisUSA1981229528.22.71.04.10.0340.1260.2200.5410.1021996-97
66Eddie JonesLAL25.0198.1286.182480TempleUSA19941108017.24.13.44.10.0350.0910.2090.5590.1491996-97
77Elden CampbellLAL28.0213.36113.398000ClemsonUSA19901277714.98.01.63.30.0950.1830.2220.5200.0871996-97
88Eldridge RecasnerATL29.0193.0486.182480WashingtonUSA1992UndraftedUndrafted715.71.61.3-0.30.0360.0760.1720.5390.1411996-97
99Elliot PerryMIL28.0182.8872.574720MemphisUSA1991237826.91.53.0-1.20.0180.0810.1770.5570.2621996-97

Last rows

Unnamed: 0player_nameteam_abbreviationageplayer_heightplayer_weightcollegecountrydraft_yeardraft_rounddraft_numbergpptsrebastnet_ratingoreb_pctdreb_pctusg_pctts_pctast_pctseason
1113511135Matisse ThybullePHI23.0195.5891.171992WashingtonUSA2019120564.61.51.21.20.0310.0470.1110.5220.0852019-20
1113611136Matt MooneyCLE23.0187.9690.264808Texas TechUSAUndraftedUndraftedUndrafted30.70.30.37.70.0000.2500.1110.5000.2002019-20
1113711137Matthew DellavedovaCLE29.0190.5090.718400St. Mary's (CA)AustraliaUndraftedUndraftedUndrafted552.81.32.9-0.90.0240.0690.1290.4450.2922019-20
1113811138Maurice HarklessNYK26.0200.6699.790240St. John's, N.Y.USA2012115595.73.91.01.90.0380.1190.1040.5750.0592019-20
1113911139Max StrusCHI23.0195.5897.522280NoneUSAUndraftedUndraftedUndrafted22.50.50.0105.80.1670.0000.1580.7270.0002019-20
1114011140Maxi KleberDAL28.0208.28108.862080NoneGermanyUndraftedUndraftedUndrafted639.15.41.14.60.0560.1400.1360.6050.0642019-20
1114111141Melvin Frazier Jr.ORL23.0195.5897.522280TulaneUSA2018235151.20.30.1-2.40.0180.0580.1640.4800.0332019-20
1114211142Meyers LeonardMIA28.0213.36117.933920IllinoisUSA2012111496.15.11.15.60.0290.2170.1200.6400.0762019-20
1114311143Norvel PellePHI27.0208.28104.779752NoneUSAUndraftedUndraftedUndrafted202.13.00.4-16.40.0850.2370.1260.5210.0562019-20
1114411144Matt ThomasTOR25.0193.0486.182480Iowa StateUSAUndraftedUndraftedUndrafted314.51.40.51.00.0170.1040.1490.6630.0892019-20